Senior AI Engineer

ZS is a place where passion changes lives. As a management consulting and technology firm focused on improving life and how we live it, we transform ideas into impact by bringing together data, science, technology and human ingenuity to deliver better outcomes for all. Here you’ll work side-by-side with a powerful collective of thinkers and experts shaping life-changing solutions for patients, caregivers and consumers, worldwide. ZSers drive impact by bringing a client-first mentality to each and every engagement. We partner collaboratively with our clients to develop custom solutions and technology products that create value and deliver company results across critical areas of their business. Bring your curiosity for learning, bold ideas, courage and passion to drive life-changing impact to ZS.

Responsibilities:

• Build, refine, and use ML Engineering platforms and components; develop and implement scalable backend systems, APIs, and microservices using FastAPI.
• Implement MLOps including model KPI measurement, tracking, model drift detection, and model feedback loops.
• Deploy and operationalize ML and Deep Learning models, with a strong focus on LLMs and Generative AI.
• Integrate Azure OpenAI (GPT-4, GPT-4 Vision) and other LLM providers with proper retry logic and error handling.
• Maintain up-to-date knowledge of state-of-the-art technologies such as LLMs, GenAI, and transformer architectures.
• Scale machine learning algorithms to work on massive data sets under strict SLAs.
• Build and orchestrate model pipelines including feature engineering, inferencing, and continuous model training.
• Write backend application code in Python and SQL using strong object-oriented principles and asynchronous programming (asyncio, async/await).
• Implement dependency injection patterns and layered architecture (Service, Foundation, Orchestration, DAL).
• Build LLM observability (e.g., Langfuse) to track prompts, tokens, costs, and latency.
• Develop prompt management systems with versioning and fallback mechanisms.
• Implement Celery (or similar) workflows for asynchronous task processing and complex pipelines.

Qualifications:

• Master’s or bachelor’s degree in Computer Science or a related field from a top university.
• 4+ years of hands-on experience in Machine Learning, including production LLM systems.
• Strong fundamentals in machine learning, deep learning, and fine-tuning models (LLMs), including:
• Understanding of transformer architectures
• Prompt engineering expertise
• Embeddings and vector search
• Experience in backend API design using FastAPI or similar asynchronous frameworks (e.g., Flask, Django), including async patterns and rate limiting.
• Experience with vector databases, including:
• Pinecone, Weaviate, or Chroma
• Embedding storage and similarity search
• Hybrid search implementations
• Strong programming expertise in Python is a must, including:
• Async programming (asyncio, async/await)
• Type hints and Pydantic
• SOLID principles and design patterns
• PySpark/Scala is optional.
• Knowledge of AI/ML concepts and experience integrating AI models into backend services is mandatory.
• Experience with MLOps to measure and track model performance, including:
• MLFlow for model tracking
• Langfuse or similar tools for LLM observability (strongly preferred)
• Model versioning and A/B testing
• Experience working with NLP and computer vision, including:
• Text extraction and preprocessing
• Document understanding (layout, tables)
• OCR processing
• GPT-4 Vision or similar multimodal integration
• Experience implementing:
• Feature engineering pipelines
• Real-time inferencing systems
• Batch prediction pipelines
• Model serving with FastAPI
• Experience with ML frameworks, including:
• HuggingFace (transformers, datasets) — mandatory
• Keras/TensorFlow/PyTorch
• LangChain — strongly preferred
• LlamaIndex for RAG
• Familiarity with database technologies such as SQL.
• Good problem-solving skills and the ability to work in a fast-paced, team-oriented environment.

Additional Skills:

• Understanding of DevOps and CI/CD, including:
• Docker containerization
• Azure DevOps pipelines or GitHub Actions
• Kubernetes (nice to have)
• Data security practices, including:
• Multi-tenant data isolation
• Secure key management (e.g., Azure Key Vault)
• Audit trail implementation
• Experience designing on cloud platforms:
• Azure (strongly preferred): Azure OpenAI, Blob Storage, Ke…